SegMatch: Segment based place recognition in 3D point clouds
Renaud Dubé, Daniel Dugas, Elena Stumm, Juan Nieto, Roland Siegwart, Cesar Cadena
Abstract
Place recognition in 3D data is a challenging task that has been commonly approached by adapting image-based solutions. Methods based on local features suffer from ambiguity and from robustness to environment changes while methods based on global features are viewpoint dependent. We propose SegMatch, a reliable place recognition algorithm based on the matching of 3D segments. Segments provide a good compromise between local and global descriptions, incorporating their strengths while reducing their individual drawbacks. SegMatch does not rely on assumptions of `perfect segmentation', or on the existence of `objects' in the environment, which allows for reliable execution on large scale, unstructured environments. We quantitatively demonstrate that SegMatch can achieve accurate localization at a frequency of 1Hz on the largest sequence of the KITTI odometry dataset. We furthermore show how this algorithm can reliably detect and close loops in real-time, during online operation. In addition, the source code for the SegMatch algorithm is made publicly available.
BibTeX
@inproceedings{icra2017_segmatchsegmentb,
title = {SegMatch: Segment based place recognition in 3D point clouds},
author = {Renaud Dubé and Daniel Dugas and Elena Stumm and Juan Nieto and Roland Siegwart and Cesar Cadena},
booktitle = {ICRA 2017},
year = {2017}
}